1st the false positive rate is probably between 1 in 100 or 1 in 1000 (0.1-1%). So @GidMK is correct. What Clare is trying to say is based upon @carlheneghan paper which states that If you do lots & lots of tests in people with you create more false positives. 1/n
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(In this case, taking a minimum value is better for your argument. The true test specificity is likely to be ~99.99% or higher, which would make your original point wrong unless the prevalence was virtually 0%)
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With a specificity of 99.95% then you would expect all of the summer ONS 'cases' to be false positive. Either that or you assume 100% specificity with R-value rock steady at 1.0000. Here is evidence so far that summer COVID was minimal -https://logicinthetimeofcovid.com/2020/09/07/waiting-for-zero/ …
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Also in these „zerocovid“ regions we might have decreased sensitivity, so you really have to control if ct is the same (40) as everywhere else. Also cave: fp come in clusters (sloppy working labs with contamination issues), you might miss them in your reference samples
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